Three-phase induction motors are considered to be the workhorse of industry. Therefore, induction motor faults are not only the cause of users’ frustrations but they also drive up the costs related to unexpected breakdowns, repair actions, and safety issues. One of the most critical faults in three-phase induction motors is related to the occurrence of inter-turn short circuits, due to its devastating consequences. The topic of inter-turn short-circuit faults in three-phase induction motors has been discussed over recent decades by several researchers. These studies have mainly dealt with early fault detection to avoid dramatic consequences. However, they fall short of addressing the potential burnout of the induction motor before the detection step. Furthermore, the cumulative action played by an inevitable degree of unbalanced supply voltages may exacerbate such consequences. For that reason, in deep detail, this paper delves into the thermal analysis of the induction motor when operating under these two harsh conditions: unbalanced supply voltages and the presence of the most incipient type of inter-turn short-circuit condition—a short-circuit between two turns only. In this work, the finite element method has been applied to create the faulty scenarios, and a commercial software (Flux2D) has been used in order to simulate the electromagnetic and thermal behavior of the machine for various degrees of severity of the aforementioned faulty modes. The obtained results confirm that the diagnostic tools reported in the literature might not be effective, failing to warrant the required lead time so that suitable actions can be taken to prevent permanent damage to the machine.

As one of the most important assets of the industry, it is crucial to fully characterise all failure modes showing potential to degrade the normal operation of induction motors (IMs). One of the failure modes which lacks detailed knowledge and proper diagnostic tools is the inter‐turn short‐circuit (SC) fault. Given the severity of such failure mode, it is pivotal to ensure that incipient fault symptoms are correctly identified, thus preventing critical damages to the IM. Unfortunately, the state‐of‐the‐art does not provide enough data to confirm whether the available diagnostic tools act out in due time to avoid permanent damage to the faulty IM. To evaluate the impacts of this failure mode in the temperature of the stator windings of an IM, this paper presents the results obtained from two alternative thermal models of the same IM, resorting to the lumped parameter thermal network method and to the finite elements method. The results confirm that diagnostic tools reported in the literature might not be effective, failing to correctly diagnose an inter‐turn SC fault and to warrant the lead time required to take actions suitable to prevent permanent damage to the IM.

This paper presents a scheme for fault detection and isolation (FDI). It deals with sensors and actuator fault of an induction machine. This scheme is established with artificial intelligent techniques in order to resolve two big troubles. The first is the detection problem. It is resolved with the neural network and the second is the isolation difficulty, it solved using the fuzzy logic. The proposed FDI approach is implemented on Matlab/Simulink software and tested under three types of fault (current, speed sensor fault and inverter fault). The obtained results improving the importance of this method. Then, the actuator and sensor fault are detected and isolated successfully.

Synchronous Reluctance Machines (SynRMs) are facing a revamped industrial interest. A comparison between SynRMs and other types of electric machines allow listing the advantages and proving the choice of such machines. This paper presents a review of SynRMs, regarding particularly their thermal analysis. First of all, a description of the basic principles of SynRM is illustrated. The thermal analysis is based mainly on the study of the heat sources and the heat extraction mechanisms which are listed and described. Nevertheless, the coupling effects between the thermal field and the magnetic material parameters are discussed. Then, the three heat extraction phenomena (conduction, convection and radiation) are analyzed. Along the work, important facts about the thermal analysis are discussed. A comparison between the different approaches used for the thermal analysis is highlighted, and new developments and perspectives are identified.

The photovoltaic (PV) system productivity is a decisive factor. To achieve a high productivity, the system availability should be checked. So even under a faulty situation, the system should operate continuously. As a proposed solution, the fault tolerant control (FTC) is recommended. In advance of FTC strategy, the fault diagnosis should be achieved. This paper deals with the open circuit fault occurred in the DC-DC converter. The Artificial Neural Network (ANN) is used to estimate the current and the voltage feeding the resistive load. These signals are compared to the measured ones. So, the proposed approach allows generating different signals in order to detect and to isolate the open circuit fault. The signals are the residuals and the flags. The fault is successfully detected and isolated.